江苏大学学报（自然科学版）2011，Vol.32Issue(6)：636-641,6.DOI:10.3969/j.issn.1671-7775.2011.06.004
基于矿质元素含量和支持向量机的茶叶鉴别分析
Identification of tea based on mineral content and support vector machines
摘要
Abstract
In order to identify variety and origin of teas, a method was proposed based on mineral content and support vector machines (SVM). The contents of Mg, Al, P, Ca, Mn, Fe, Cu, Zn and Ba were analyzed by ICP-OES and were normalized. The data were collected randomly as learning samples for designing and training multielement classifier to identify tea variety and origin by SVM. The results show that classification method which is based on "one versus one" multi-class support vector machine has better classification ability and stronger anti-jamming capability than that of cluster analysis. For small samples, the tea variety and origin identification accuracy can reach 91. 67% , which illuminates that the method is effective for indentifying tea variety and origin.关键词
茶叶/矿质元素/支持向量机/鉴别/产地/种类Key words
tea/mineral element/support vector machines/identification/origin/variety分类
轻工纺织引用本文复制引用
李清光,李晓钟,钟芳..基于矿质元素含量和支持向量机的茶叶鉴别分析[J].江苏大学学报（自然科学版）,2011,32(6):636-641,6.基金项目
教育部人文社会科学研究规划基金资助项目(10YJA790098) （10YJA790098）
江苏省普通高校研究生科研创新计划项目(cx10B_232z) （cx10B_232z）
江南大学博士研究生科学研究基金资助项目(JUDCF10010) （JUDCF10010）
